Abstract: Validating a simulation scenario for use in training a machine learning model for an autonomous vehicle includes determining a simulation scenario; executing a simulation based on a simulation scenario; monitoring execution of the simulation and receiving messages from the execution of the simulation; determining, by a first simulation monitor, whether the messages satisfy a first incident; and validating the simulation scenario responsive to the messages satisfying the first incident to produce validated simulation data. A system and method may also include determining, by a first simulation validator, whether the messages satisfy a condition; and validating the simulation scenario responsive to the messages satisfying the condition to produce validated simulation data.

Abstract: Validating a simulation scenario for use in training a machine learning model for an autonomous vehicle includes determining a simulation scenario; executing a simulation based on a simulation scenario; monitoring execution of the simulation and receiving messages from the execution of the simulation; determining, by a first simulation monitor, whether the messages satisfy a first incident; and validating the simulation scenario responsive to the messages satisfying the first incident to produce validated simulation data. A system and method may also include determining, by a first simulation validator, whether the messages satisfy a condition; and validating the simulation scenario responsive to the messages satisfying the condition to produce validated simulation data.

Abstract: Logged data from an autonomous vehicle is processed to generate augmented data. The augmented data describes an actor in an environment of the autonomous vehicle, the actor having an associated actor type and an actor motion behavior characteristic. The augmented data may be varied to create different sets of augmented data. The sets of augmented data can be used to create one or more simulation scenarios that in turn are used to produce machine learning models to control the operation of autonomous vehicles.

Abstract: Simulation data of the autonomous vehicle is processed by executing a simulation based on the simulation data to generate a simulation result. Then a perception scenario is generated from the simulation result. The generated perception scenario is validated by verifying whether a constraint is satisfied to produce a validated perception scenario. The validated perception scenario can be used to create or refine a perception model used for controlling the operation of autonomous vehicles.

Abstract: The current invention relates to a method for the production of at least one organometallic compound comprising: filling solid metal particulates or metal containing solid particulates into a reactor column obtaining a metal bed; continuously contacting a fluid of at least one substrate, preferably the fluid comprises a water-free solvent, with the metal bed; transporting the fluid against gravity through the metal bed, characterized in that, the fluid is partially recirculated again over the metal bed. The invention also relates to a device for the production of at least one organometallic compound, the organometallic compound itself and chemical substances produced from said compound.

Abstract: Simulation data of the autonomous vehicle is processed by executing a simulation based on the simulation data to generate a simulation result. Then a perception scenario is generated from the simulation result. The generated perception scenario is validated by verifying whether a constraint is satisfied to produce a validated perception scenario. The validated perception scenario can be used to create or refine a perception model used for controlling the operation of autonomous vehicles.

Abstract: Logged data from an autonomous vehicle is processed to generate augmented data. The augmented data describes an actor in an environment of the autonomous vehicle, the actor having an associated actor type and an actor motion behavior characteristic. The augmented data may be varied to create different sets of augmented data. The sets of augmented data can be used to create one or more simulation scenarios that in turn are used to produce machine learning models to control the operation of autonomous vehicles.

Abstract: Logged data from an autonomous vehicle is processed to generate augmented data. The augmented data describes an actor in an environment of the autonomous vehicle, the actor having an associated actor type and an actor motion behavior characteristic. The augmented data may be varied to create different sets of augmented data. The sets of augmented data can be used to create one or more simulation scenarios that in turn are used to produce machine learning models to control the operation of autonomous vehicles.

Abstract: Logged data from an autonomous vehicle is processed to generate augmented data. The augmented data describes an actor in an environment of the autonomous vehicle, the actor having an associated actor type and an actor motion behavior characteristic. The augmented data may be varied to create different sets of augmented data. The sets of augmented data can be used to create one or more simulation scenarios that in turn are used to produce machine learning models to control the operation of autonomous vehicles.

Abstract: Logged data from an autonomous vehicle is processed to generate augmented data. The augmented data describes an actor in an environment of the autonomous vehicle, the actor having an associated actor type and an actor motion behavior characteristic. The augmented data may be varied to create different sets of augmented data. The sets of augmented data can be used to create one or more simulation scenarios that in turn are used to produce machine learning models to control the operation of autonomous vehicles.

Abstract: Logged data from an autonomous vehicle is processed to generate augmented data. The augmented data describes an actor in an environment of the autonomous vehicle, the actor having an associated actor type and an actor motion behavior characteristic. The augmented data may be varied to create different sets of augmented data. The sets of augmented data can be used to create one or more simulation scenarios that in turn are used to produce machine learning models to control the operation of autonomous vehicles.

Abstract: Logged data from an autonomous vehicle is processed to generate augmented data. The augmented data describes an actor in an environment of the autonomous vehicle, the actor having an associated actor type and an actor motion behavior characteristic. The augmented data may be varied to create different sets of augmented data. The sets of augmented data can be used to create one or more simulation scenarios that in turn are used to produce machine learning models to control the operation of autonomous vehicles.

Abstract: Logged data from an autonomous vehicle is processed to generate augmented data. The augmented data describes an actor in an environment of the autonomous vehicle, the actor having an associated actor type and an actor motion behavior characteristic. The augmented data may be varied to create different sets of augmented data. The sets of augmented data can be used to create one or more simulation scenarios that in turn are used to produce machine learning models to control the operation of autonomous vehicles.

Abstract: A method for state estimation of a road network includes at least the steps of gathering information from at least two sensors, wherein at least one of the sensors detects radio signals, combining the information from the at least two sensors using an extended Kalman filter, and determining at least one state in a discretized road network using the combined information.

Abstract: A security barrier (10) comprises a first fence (20) and a second fence (40). The first fence comprises a first series of posts (22) and the second fence comprises a second series of posts (42). The first and second fences are provided adjacent to each other with the first fence being coupled below ground level with a the second fence. A footing having first and second post sockets is also provided for receiving a first post from each of the first and second fences respectively.